With the development of video image processing means, the images from video surveillance are increasingly analyzed by a video processing algorithm. A video surveillance system can notably integrate an algorithm to track objects of interest, such as individuals, in a sequence of images. These objects of interest are called targets. An object tracking algorithm must deal with various constraints, and particularly with temporary target occlusions. These occlusions may be due to the stationary elements of the scene, in such a case, reference is made to environmental occlusion, or to mobile elements such as people, in which case reference is made to object occlusion. An occlusion can be partial, if the target remains partially visible, or total, if the target is completely invisible in the scene. For example, in the case of a partial occlusion of a person, the head and chest may remain visible, whereas the legs are hidden by another person closer to the camera. When significant occlusion occurs, the tracking algorithm cannot observe the target that it is supposed to track in the scene. This results in an error in the location of the target with generally increasingly spreads throughout the duration of the occlusion. This error may become irreversible if the tracking algorithm has difficulty distinguishing the target from other objects that hide it. The case can, for example, occur if the target is similar in appearance to that of other objects.
In a so-called “multi-target” tracking context, that is, when several objects of interest, or all the moving objects in the scene, are tracked simultaneously, the interactions between the objects can be modeled based on their movements, and thus it is possible to determine the occlusions present. For example, patent application GB 2 452 512 A (GB 07 17277) discloses a method for managing occlusions comprising a re-identification step. This re-identification does not necessarily take place as soon as the objects involved in the occlusion become separated. As long as the ambiguity due to the occlusion has not been resolved, the paths of all the objects involved in the occlusion are labeled with the identifiers. The ambiguity persists for a few images, until the various targets are identified once again. The path of each target can subsequently be retrieved by going back in time. Another solution for managing the tracking of objects in the presence of an occlusion is based on a global association, that is the simultaneous use of multiple target trajectories. Each trajectory consists of a sequence of track segments, called “tracklets”. Global association techniques are very efficient, but at a high computational cost making them unsuitable for real-time tracking. The document by Junliang Xing, Haizhou Ai, Shihong Lao: “Multi-Object Tracking through Occlusions by Local Tracklets Filtering and Global Tracklets Association with Detection Responses”, Computer Vision and Pattern Recognition, 2009, describes the use of a tracking algorithm by particle filtering supported by a global association method in a reduced temporal window. This solution makes it possible to maintain real-time processing and ensure overall optimization of object trajectories. The detection-based filter determines the optimal position of the targets, and therefore their track, and selects the most significant observations for each target. At the same time, the observations detected by a people classifier are associated image by image according to the affinity in the position, the size and appearance, by generating potential tracklets. These potential tracklets are associated with the tracks from the filter in order to maximize the final trajectories. This global association step is performed based on similarities in track size, appearance and dynamics. When a target can no longer be observed, due to an occlusion for example, the filter is stopped and the track can only be continued through global association with a potential detection-based tracklet. One limitation of this solution stems from absolute confidence in the filter. As long as the filter responds positively to its observation model, its result is not checked, the global association being used only to fill in any blanks in the trajectories provided by the filter.
The situation is more complex for so-called “single-target” tracking, where a single object of interest, a person for example, is tracked, although several other objects are moving in the scene. In this context, the observations of elements next to the target, which are not being tracked, are not allocated to objects. Thus, when the target is obscured, these observations can be wrongly attributed to the target. The same problem arises with multi-target tracking, when certain objects are not tracked. However, robust multi-target tracking in relation to occlusions and based on global association techniques may prove difficult in real time in a busy public location, such as a train station or an airport. Real-time tracking is particularly important when security officers must be informed of the location of a suspicious individual. An individual can notably become suspect when he/she enters a prohibited area, abandons a piece of luggage or assaults someone.